Unverified50% confidenceFactExact time
Small language models running on devices often struggle to stably generate target grammatical structures.
1
Sources
50%
Confidence
Long-term
Relevance
10/30/2026
First Seen
Sources
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Related Claims
VerifiedLarge language models are architecturally incapable of strictly distinguishing between system instructions and user data, which is a fundamental flaw at the architectural level.78% similarUnverifiedLanguage models often struggle to strictly distinguish between 'rules in the system prompt' and 'new instructions in the user message'.75% similarUnverifiedWhen using a model to polish or rewrite text, simply asking it to preserve the original tone and wording is often insufficient; the more fundamental approach is to rephrase the output in one's own words afterward.74% similarVerifiedThe bottleneck of large language model inference is often not computational power, but memory bandwidth.74% similarUnverifiedIn scenarios requiring deep reasoning or multi-turn long-context understanding, locally running small language models generally produce lower-quality answers than cloud-based large models.74% similar
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